Abstract
Identifying and extracting data elements such as study descriptors in publication full texts is a critical yet manual and labor-intensive step required in a number of tasks. In this paper we address the question of identifying data elements in an unsupervised manner. Specifically, provided a set of criteria describing specific study parameters, such as species, route of administration, and dosing regimen, we develop an unsupervised approach to identify text segments (sentences) relevant to the criteria. A binary classifier trained to identify publications that met the criteria performs better when trained on the candidate sentences than when trained on sentences randomly picked from the text, supporting the intuition that our method is able to accurately identify study descriptors.
| Original language | English |
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| Title of host publication | EMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 71-82 |
| Number of pages | 12 |
| ISBN (Electronic) | 9781948087742 |
| State | Published - 2018 |
| Event | 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018 - Brussels, Belgium Duration: Oct 31 2018 → … |
Publication series
| Name | EMNLP 2018 - 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018 - Proceedings of the Workshop |
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Conference
| Conference | 9th International Workshop on Health Text Mining and Information Analysis, LOUHI 2018, co-located with EMNLP 2018 |
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| Country/Territory | Belgium |
| City | Brussels |
| Period | 10/31/18 → … |
Bibliographical note
Publisher Copyright:© 2018 Association for Computational Linguistics.
Funding
Support for this research was provided by a grant from the National Institute of Environmental Health Sciences (AES 16002-001), National Institutes of Health to Oak Ridge National Laboratory. This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan1.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| National Institute of Environmental Health Sciences | AES 16002-001 |
| Oak Ridge Institute for Science and Education |